China’s Rise: GDP Ranking Changes (1960-2020)

From 5th to 2nd: The WTO Effect on China’s Economic Ascen
Values shown in trillion USD (2020)

30DayChartChallenge
Data Visualization
R Programming
2025
A bump chart visualization showcasing China’s remarkable economic rise from 1960 to 2020, highlighting how WTO membership accelerated its ascent from 8th to 2nd in global GDP rankings.
Author

Steven Ponce

Published

April 5, 2025

Figure 1: A bump chart showing GDP ranking changes from 1960-2020 for major economies. China (in bold red) started at 5th place in 1960, dropped to 8th by 1980, began rising after joining the WTO (highlighted in pink), and reached 2nd place by 2020. The US remains consistently in 1st place throughout the period, while Japan and other economies show various ranking changes. GDP values in trillion USD for 2020 are displayed.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
  tidyverse,      # Easily Install and Load the 'Tidyverse'
  ggtext,         # Improved Text Rendering Support for 'ggplot2'
  showtext,       # Using Fonts More Easily in R Graphs
  janitor,        # Simple Tools for Examining and Cleaning Dirty Data
  skimr,          # Compact and Flexible Summaries of Data
  scales,         # Scale Functions for Visualization
  lubridate,      # Make Dealing with Dates a Little Easier
  ggbump,         # Bump Chart and Sigmoid Curves
  ggrepel,        # Automatically Position Non-Overlapping Text Labels with 'ggplot2'
  WDI,            # World Development Indicators and Other World Bank Data
  camcorder       # Record Your Plot History
)
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 10,
    height = 8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code
# Get GDP data (current US$) for top economies
gdp_data <- WDI(
  indicator = c("gdp" = "NY.GDP.MKTP.CD"),                # GDP in current US$
  country = c("US", "CN", "JP", "DE", "GB", "IN", "FR", "IT", 
              "BR", "CA", "KR", "RU", "AU", "ES", "MX"),
  start = 1960,
  end = 2020
)

3. Examine the Data

Show code
glimpse(gdp_data)
skim(gdp_data)

4. Tidy Data

Show code
### |- Tidy ----
# Filter parameters
countries_to_keep <- c("US", "CN", "JP", "DE", "GB", "IN", "FR", "IT", "KR", "BR")
years_to_keep <- c(1960, 1970, 1980, 1990, 2000, 2010, 2020)

# Filter the data
gdp_filtered <- gdp_data |>
  filter(iso2c %in% countries_to_keep) |>
  filter(year %in% years_to_keep) |>
  filter(!is.na(gdp))

# Calculate rankings for each year
gdp_ranked <- gdp_filtered |>
  group_by(year) |>
  mutate(rank = rank(-gdp, ties.method = "first")) |>
  ungroup()

# Classify countries into highlighted vs. background
gdp_ranked <- gdp_ranked |>
  mutate(
    # Only 3 highlighted countries (US, China, Japan)
    highlight_group = case_when(
      iso2c == "US" ~ "US",
      iso2c == "CN" ~ "China",
      iso2c == "JP" ~ "Japan",
      TRUE ~ "Other"
    ),
    # Create a size variable for lines and points
    line_size = case_when(
      iso2c == "CN" ~ 2.5,              # China gets thickest line
      iso2c %in% c("US", "JP") ~ 1.5,   # US and Japan medium
      TRUE ~ 0.8                        # Others thin
    ),
    point_size = case_when(
      iso2c == "CN" ~ 5,                # China gets largest points
      iso2c %in% c("US", "JP") ~ 3.5,   # US and Japan medium
      TRUE ~ 2                          # Others small
    ),
    # Alpha for background countries
    line_alpha = case_when(
      iso2c %in% c("US", "CN", "JP") ~ 1,
      TRUE ~ 0.5
    ),
    # Country labels
    country_label = case_when(
      iso2c == "US" ~ "United\nStates",
      iso2c == "CN" ~ "China",
      iso2c == "JP" ~ "Japan",
      iso2c == "DE" ~ "Germany",
      iso2c == "GB" ~ "United\nKingdom",
      iso2c == "IN" ~ "India",
      iso2c == "FR" ~ "France",
      iso2c == "IT" ~ "Italy",
      iso2c == "KR" ~ "South\nKorea",
      iso2c == "BR" ~ "Brazil",
      TRUE ~ country
    ),
    # Format GDP in trillions 
    gdp_trillion = round(gdp / 1e12, 2),
    # GDP label
    gdp_label = paste0("$", gdp_trillion, "T")
  )

# Left label dataset 
left_labels <- gdp_ranked |> 
  filter(year == 1960) |>
  mutate(
    # horizontal adjustments
    hjust = 1,
    nudge_x = -1.5,
    nudge_y = 0
   
  )

# Right label dataset 
right_labels <- gdp_ranked |> 
  filter(year == 2020) |>
  mutate(
    label_line = str_glue("{ country_label } ({ gdp_label })"),
    # horizontal adjustments
    hjust = 0,
    nudge_x = 1.5,
    nudge_y = 0
  )

5. Visualization Parameters

Show code
### |- plot aesthetics ---- 
colors <- get_theme_colors(palette = c(
  "US" = "#0066CC",      
  "China" = "#CC0000",   
  "Japan" = "#FF9900",   
  "Other" = "#999999"   
  )
)

### |-  titles and caption ----
# text
title_text    <- str_glue("China's Rise: GDP Ranking Changes (1960-2020)") 
subtitle_text <- str_glue("From 5th to 2nd: The WTO Effect on China's Economic Ascen<br>
                          Values shown in trillion USD (2020)")

# Create caption
caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 05,
  source_text =  "{ WDI } World Bank data in R" 
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling 
    plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
    plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
    
    # Axis elements
    axis.title.y = element_text(color = colors$text, size = rel(0.8),
                              hjust = 1, vjust = 0.95, angle = 0),
    axis.title.x = element_blank(),
    axis.text.x = element_text(color = colors$text, size = rel(0.7)),
    axis.line.x = element_line(color = "gray50", linewidth = .2),

    # Grid elements
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
 
    # Legend elements
    legend.position = "plot",
    legend.title = element_text(family = fonts$text, size = rel(0.8)),
    legend.text = element_text(family = fonts$text, size = rel(0.7)),
    
    # Plot margins 
    plot.margin = margin(t = 10, r = 20, b = 10, l = 20),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code
### |-  Plot ----
p <- ggplot(gdp_ranked, aes(x = year, y = rank, group = country_label)) +
  # Add background shading
  annotate("rect",
    xmin = 1995, xmax = 2005, ymin = 0, ymax = 11,
    fill = "#FF8B8B", alpha = 0.15
  ) +
  # Geoms
  geom_bump(aes(color = highlight_group, size = line_size, alpha = line_alpha), smooth = 8) +
  geom_point(aes(color = highlight_group, size = point_size, alpha = line_alpha)) +
  geom_text(                                                                    # left side labels
    data = left_labels,
    aes(label = country_label, color = highlight_group, y = rank + nudge_y),
    hjust = 1,
    nudge_x = -2,
    lineheight = 0.9,
    size = 3.2,
  ) +
  geom_text(                                                                    # right side labels
    data = right_labels,
    aes(label = label_line, color = highlight_group, y = rank), 
    hjust = 0,
    nudge_x = 2,
    size = 2.8,
    fontface = "bold"
  ) +
  # Annotate
  annotate("text",
    x = 2001, y = 6.7, label = "China joins WTO", color = "gray20",
    size = 3.2, fontface = "italic"
  ) +
  annotate("segment",
    x = 2000, xend = 2000, y = 6.5, yend = 6.1,
    arrow = arrow(length = unit(0.2, "cm")), linewidth = 0.5, color = "gray20"
  ) +
  # Scales
  scale_y_reverse(breaks = 1:10) +
  scale_x_continuous(
    breaks = c(1960, 1970, 1980, 1990, 2000, 2010, 2020),
    limits = c(1955, 2029)
  ) +
  scale_color_manual(values = colors$palette) +
  scale_size_identity() +
  scale_alpha_identity() +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    x = "Year",
    y = "Rank",
    caption = caption_text
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.8),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(1),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 5)
    ),
    plot.caption = element_markdown(
      size = rel(0.6),
      family = fonts$caption,
      color = colors$caption,
      lineheight = 0.65,
      hjust = 0.5,
      halign = 0.5,
      margin = margin(t = 10, b = 5)
    ),
  )

7. Save

Show code
### |-  plot image ----  

save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 05, 
  width = 10, 
  height = 8
  )

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      camcorder_0.1.0 WDI_2.7.9       ggrepel_0.9.6  
 [5] ggbump_0.1.0    scales_1.3.0    skimr_2.1.5     janitor_2.2.0  
 [9] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
[13] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[17] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[21] ggplot2_3.5.1   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.4.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] gifski_1.32.0-1   fansi_1.0.6       pacman_0.5.1      pkgconfig_2.0.3  
[13] lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0    textshaping_0.4.0
[17] munsell_0.5.1     repr_1.1.7        codetools_0.2-20  snakecase_0.11.1 
[21] htmltools_0.5.8.1 yaml_2.3.10       pillar_1.9.0      magick_2.8.5     
[25] commonmark_1.9.2  tidyselect_1.2.1  digest_0.6.37     stringi_1.8.4    
[29] rsvg_2.6.1        rprojroot_2.0.4   fastmap_1.2.0     grid_4.4.0       
[33] colorspace_2.1-1  cli_3.6.3         magrittr_2.0.3    base64enc_0.1-3  
[37] utf8_1.2.4        withr_3.0.2       timechange_0.3.0  rmarkdown_2.29   
[41] ragg_1.3.3        hms_1.1.3         evaluate_1.0.1    knitr_1.49       
[45] markdown_1.13     rlang_1.1.4       gridtext_0.1.5    Rcpp_1.0.13-1    
[49] glue_1.8.0        xml2_1.3.6        renv_1.0.3        svglite_2.1.3    
[53] rstudioapi_0.17.1 jsonlite_1.8.9    R6_2.5.1          systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in 30dcc_2025_05.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
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